Data preprocessing involves several key steps: 1) Data cleaning to fill in missing values, identify and remove outliers, and resolve inconsistencies 2) Data integration to combine multiple data sources and resolve conflicts and redundancies 3) Data reduction techniques like discretization, dimensionality reduction, and aggregation to obtain a reduced representation of the data for mining and analysis.

2. 
Why preprocess the data?

Descriptive data summarization

Data cleaning

Data integration and transformation

Data reduction

Discretization and concept hierarchy generation

Summary

3. 
Data in the real world is dirty

incomplete: lacking attribute values, lacking certain
attributes of interest, or containing only aggregate
data
 Ex-

noisy: containing errors or outliers
 Ex-

occupation=― ‖
Salary=―-10‖
inconsistent: containing discrepancies in codes or
names
 Ex- Age=―42‖
Birthday=―03/07/1997‖

4. 
Incomplete data may come from




―Not applicable‖ data value when collected
Different considerations between the time when the data
was collected and when it is analyzed.
Human/hardware/software problems
Noisy data (incorrect values) may come from



Faulty data collection instruments
Human or computer error at data entry
Errors in data transmission

5. 
Inconsistent data may come from



Different data sources
Functional dependency violation (e.g., modify some
linked data)
Duplicate records also need data cleaning

6. 
No quality data, gives no quality mining results.

Quality decisions must be based on quality data
Ex- duplicate or missing data may
cause incorrect or even misleading statistics.


Data warehouse needs consistent integration of
quality data.
Data extraction, cleaning, and transformation
comprises the majority of the work of building a data
warehouse

7. 
A well-accepted multidimensional view:
 Accuracy
 Completeness
 Consistency
 Timeliness
 Believability
 Value added
 Interpretability
 Accessibility

Broad categories:
 Intrinsic, contextual, representational, and
accessibility

8. 
Data cleaning
 Fill in missing values, smooth noisy data, identify
or remove outliers, and resolve inconsistencies.

Data integration
 Integration of multiple databases, data cubes, or
files.

Data transformation
 Normalization and aggregation

9. 
Data reduction
 Obtains reduced representation in volume but
produces the same or similar analytical results.

Data discretization
 Part of data reduction but with particular
importance, especially for numerical data.

11. 

To better understand the data: central tendency,
variation and spread.
characteristics :
 median, max, min, quantiles, outliers, variance, etc.
 Numerical
dimensions correspond to sorted intervals

Data dispersion: analyzed with multiple
granularities of precision

Boxplot or quantile analysis on sorted intervals

12.  Dispersion analysis on
computed measures

Folding measures into numerical dimensions

Boxplot or quantile analysis on the transformed
cube

13.  Mean

(algebraic measure) (sample vs. population):
Weighted arithmetic mean:
n
1
x
n

x
xi
i 1
N
Trimmed mean: chopping extreme values.
n
wi xi
x
i 1
n
wi
i 1

14.  Median: A holistic measure

Middle value if odd number of values, or average
of the middle two values otherwise

Estimated by interpolation (for grouped data):
 Mode
median
L1
(
n/2
(
f )l
)c
f median

Value that occurs most frequently in the data

Unimodal, bimodal, trimodal

Empirical formula:
mean
mode
3 ( mean
median )

15.  Quartiles, outliers and
boxplots

Quartiles: Q1 (25th percentile), Q3 (75th percentile)

Inter-quartile range: IQR = Q3 – Q1

Five number summary: min, Q1, M, Q3, max

Boxplot: ends of the box are the quartiles, median
is marked, whiskers, and plot outlier individually

Outlier: usually, a value higher/lower than 1.5 x
IQR

16.  Variance and
standard deviation (sample: s,
population: σ)

Variance: (algebraic, scalable computation)

Standard deviation s (or σ) is the square root of
variance s2 (or σ2)

17. 
Five-number summary of a distribution:
Minimum, Q1, M, Q3, Maximum

Boxplot

Data is represented with a box

The ends of the box are at the first and third
quartiles, i.e., the height of the box is IRQ

The median is marked by a line within the box

Whiskers: two lines outside the box extend to
Minimum and Maximum

19. 
Data cleaning tasks are

Fill in missing values

Identify outliers and smooth out noisy data

Correct inconsistent data

Resolve redundancy caused by data integration

20.  Data

is not always available
Ex- many tuples have no recorded value for several
attributes, such as customer income in sales data.
 Missing
data may be due to

equipment malfunction

inconsistent with other recorded data and thus
deleted.

21. 
data not entered due to misunderstanding

certain data may not be considered important at the
time of entry

not register history or changes of the data.
 Missing
data may need to be inferred.

22. Noise: random error or variance in a measured
variable.
 Incorrect attribute values may due to
 faulty data collection instruments
 data entry problems
 data transmission problems
 technology limitation
 inconsistency in naming convention
 Other data problems which requires data cleaning.
 duplicate records
 incomplete data
 inconsistent data


23. Binning
 first sort data and partition into (equal-frequency)
bins
 then one can smooth by bin means, smooth by bin
median, smooth by bin boundaries, etc.
 Regression
 smooth by fitting the data into regression functions
 Clustering
 detect and remove outliers
 Combined computer and human inspection
 detect suspicious values and check by human (e.g.,
deal with possible outliers)


24. 
Equal-width (distance) partitioning

Divides the range into N intervals of equal size:
uniform grid

if A and B are the lowest and highest values of the
attribute, the width of intervals will be: W = (B –
A)/N.

The most straightforward, but outliers may
dominate presentation

Skewed data is not handled well

25. 
Equal-depth (frequency) partitioning

Divides the range into N intervals, each containing
approximately same number of samples

Good data scaling

Managing categorical attributes can be tricky

26. Sorted data for price (in dollars): 4, 8, 9, 15, 21, 21, 24,
25, 26, 28, 29, 34
* Partition into equal-frequency (equi-depth) bins:
- Bin 1: 4, 8, 9, 15
- Bin 2: 21, 21, 24, 25
- Bin 3: 26, 28, 29, 34
* Smoothing by bin means:
- Bin 1: 9, 9, 9, 9
- Bin 2: 23, 23, 23, 23
- Bin 3: 29, 29, 29, 29


27. Smoothing by bin boundaries:
- Bin 1: 4, 4, 4, 15
- Bin 2: 21, 21, 25, 25
- Bin 3: 26, 26, 26, 34

28. Data integration:
 Combines data from multiple sources into a
coherent store
 Schema integration: Ex- A.cust-id B.cust-#
 Integrate metadata from different sources
 Entity identification problem:
 Identify real world entities from multiple data
sources, Ex- Bill Clinton = William Clinton
 Detecting and resolving data value conflicts
 For the same real world entity, attribute values
from different sources are different
 Possible reasons: different representations,
different scales, Ex- metric vs. British units


29. 
Redundant data occur often when integration of
multiple databases


Object identification: The same attribute or object may
have different names in different databases
Derivable data: One attribute may be a ―derived‖ attribute
in another table, Ex- annual revenue

Redundant attributes may be able to be detected by
correlation analysis

Careful integration of the data from multiple sources
may help reduce/avoid redundancies and
inconsistencies and improve mining speed and quality

30. 
Smoothing: remove noise from data

Aggregation: summarization, data cube construction

Generalization: concept hierarchy climbing

Normalization: scaled to fall within a small, specified
range


z-score normalization


min-max normalization
normalization by decimal scaling
Attribute/feature construction

New attributes constructed from the given ones

31. Data reduction
 Obtain a reduced representation of the data set that
is much smaller in volume but yet produce the
same (or almost the same) analytical results
 Data reduction strategies
 Data cube aggregation:
 Dimensionality reduction — e.g., remove
unimportant attributes
 Data Compression
 Numerosity reduction — e.g., fit data into models
 Discretization and concept hierarchy generation


32. 
The lowest level of a data cube (base cuboid)



The aggregated data for an individual entity of interest
E.g., a customer in a phone calling data warehouse
Multiple levels of aggregation in data cubes


Reference appropriate levels


Further reduce the size of data to deal with
Use the smallest representation which is enough to solve
the task
Queries regarding aggregated information should be
answered using data cube, when possible

33. 
Feature selection (i.e., attribute subset selection):
Select a minimum set of features such that the
probability distribution of different classes given
the values for those features is as close as possible
to the original distribution given the values of all
features
 reduce # of patterns in the patterns, easier to
understand


Heuristic methods (due to exponential # of choices):
Step-wise forward selection
 Step-wise backward elimination
 Combining forward selection and backward
elimination
 Decision-tree induction


34. 
Given N data vectors from n-dimensions, find k ≤ n
orthogonal vectors (principal components) that can be
best used to represent data .

Steps
 Normalize input data: Each attribute falls within
the same range
 Compute k orthonormal (unit) vectors, i.e.,
principal components
 Each input data (vector) is a linear combination of
the k principal component vectors

35. 
The principal components are sorted in order of
decreasing ―significance‖ or strength.

Since the components are sorted, the size of the
data can be reduced by eliminating the weak
components, i.e., those with low variance. (i.e.,
using the strongest principal components, it is
possible to reconstruct a good approximation of the
original data.

Works for numeric data only.

Used when the number of dimensions is large.

36. 
Data preparation or preprocessing is a big issue for
both data warehousing and data mining

Discriptive data summarization is need for quality data
preprocessing

Data preparation includes


Data reduction and feature selection


Data cleaning and data integration
Discretization
A lot a methods have been developed but data
preprocessing still an active area of research

37. THANK YOU